A new research review published in BJOG: An International Journal of Obstetrics & Gynecology, suggests that tools designed to predict life-threatening complications during pregnancy and shortly after birth may one day help identify risks in a range of conditions, including disorders like fetal and neonatal alloimmune thrombocytopenia (FNAIT). However, these tools are not yet reliable enough for routine use.
The analysis looked at 13 studies that tested different ways of identifying women at risk of severe maternal outcomes, including death or near-fatal complications. These tools are especially important in non-intensive care settings, such as regular maternity wards or community care, where most women receive care but monitoring and specialist support may be limited.
Across the studies, the models showed a moderate to strong ability to distinguish between higher- and lower-risk patients. Common predictors included maternal age, underlying heart disease, creatinine levels, respiratory rate and mode of delivery.
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However, the review found that all of the models assessed had a high risk of bias. Many studies included too few participants, handled missing data poorly or used less reliable methods to select which risk factors were studied. These limitations raise questions about how well the tools would perform in real-world clinical settings.
Many models also lacked proper internal validation, and only a small number were tested in external populations. None of the studies assessed calibration, meaning it remains unclear how closely predicted risks matched actual outcomes.
The review also found that most research has been done in hospitals, with very little evidence from community settings, where early warning tools could help prevent complications before they become severe.
The researchers note that better-designed studies are needed to create more accurate and practical tools.
“Implementation studies assessing workflow integration and clinical impact are essential before deployment,” the authors wrote. “Addressing these gaps is crucial for enhancing risk prediction in non-ICU environments, improving risk stratification, guiding resource allocation, and ultimately contributing to the reduction of global maternal morbidity and mortality.”
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